add parallel ops for expand dims

mindspore/ccsrc/parallel/dynamic_creator.h

@@ -125,6 +125,7 @@ REGISTER(SqrtInfo);
 REGISTER(GetNextInfo);
 REGISTER(NegInfo);
 REGISTER(BatchMatMulInfo);
+REGISTER(ExpandDimsInfo);
 }  // namespace parallel
 }  // namespace mindspore
 

mindspore/ccsrc/parallel/ops_info/activation_info.cc

@@ -381,5 +381,168 @@ Status CastInfo::InferMirrorOps() {
 
   return SUCCESS;
 }
+
+Status ExpandDimsInfo::GetAttrs() {
+  if (input_value_.size() != EXPANDDIMS_INPUT_SIZE) {
+    MS_LOG(ERROR) << name_ << ": Invalid inputs size " << input_value_.size();
+    return FAILED;
+  }
+
+  if (!input_value_.back()->isa<Int32Imm>()) {
+    MS_LOG(ERROR) << name_ << ": The type of axis is not int";
+    return FAILED;
+  }
+
+  int32_t axis = GetValue<int32_t>(input_value_.back());
+
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
+    return FAILED;
+  }
+
+  int32_t dim = SizeToInt(inputs_shape_[0].size());
+  if ((axis > dim) || (axis < -dim - 1)) {
+    MS_LOG(ERROR) << name_ << ": The axis(" << axis << ") is out of range[" << -dim - 1 << ", " << dim << "]";
+    return FAILED;
+  }
+
+  if (axis < 0) {
+    positive_axis_ = dim + axis + 1;
+  } else {
+    positive_axis_ = axis;
+  }
+  MS_LOG(INFO) << name_ << ": The axis is " << axis << ", and the positive axis is " << positive_axis_;
+  return SUCCESS;
+}
+
+Status ExpandDimsInfo::InferTensorMap() {
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
+    return FAILED;
+  }
+
+  // for example: if the dimension of input is 3, and the axis is 2,
+  // then the input_tensor_map is [2, 1, 0], the output_tensor_map is [2, 1, -1, 0]
+  std::vector<int32_t> input_tensor_map, output_tensor_map;
+  size_t size = inputs_shape_[0].size();
+  for (size_t i = 0; i < size; ++i) {
+    input_tensor_map.push_back(SizeToInt(size - i - 1));
+  }
+
+  inputs_tensor_map_.push_back(input_tensor_map);
+
+  output_tensor_map = input_tensor_map;
+  if ((positive_axis_ < 0) || (positive_axis_ > SizeToInt(size))) {
+    MS_LOG(ERROR) << name_ << ": Invalid positive axis " << positive_axis_;
+    return FAILED;
+  }
+  (void)output_tensor_map.insert(output_tensor_map.begin() + positive_axis_, NO_SPLIT_MAP);
+  outputs_tensor_map_.push_back(output_tensor_map);
+
+  MS_LOG(INFO) << name_ << ": The tensor map of input is " << ShapeToString(input_tensor_map)
+               << ", and the tensor map of output is " << ShapeToString(output_tensor_map);
+  return SUCCESS;
+}
+
+Status ExpandDimsInfo::InferTensorStrategy() {
+  if (strategy_ == nullptr) {
+    MS_LOG(ERROR) << name_ << ": The strategy is null";
+    return FAILED;
+  }
+
+  inputs_strategy_ = strategy_->GetInputDim();
+  if (inputs_strategy_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The strategy is empty";
+    return FAILED;
+  }
+
+  Shape output_strategy = inputs_strategy_[0];
+  if ((positive_axis_ < 0) || (positive_axis_ > SizeToInt(output_strategy.size()))) {
+    MS_LOG(ERROR) << name_ << ": Invalid positive axis " << positive_axis_;
+    return FAILED;
+  }
+  (void)output_strategy.insert(output_strategy.begin() + positive_axis_, NO_SPLIT_STRATEGY);
+  outputs_strategy_ = {output_strategy};
+  return SUCCESS;
+}
+
+Status ExpandDimsInfo::InferTensorInfo() {
+  if (inputs_shape_.empty() || outputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The shape of inputs or outputs is empty";
+    return FAILED;
+  }
+
+  if (inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The tensor map of inputs or outputs is empty";
+    return FAILED;
+  }
+
+  Shape input_shape = inputs_shape_[0];
+  Shape output_shape = outputs_shape_[0];
+
+  // infer slice shape
+  if (InferTensorStrategy() != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer tensor strategy failed";
+    return FAILED;
+  }
+  Shapes inputs_slice_shape, outputs_slice_shape;
+  if (InferSliceShape(inputs_strategy_, outputs_strategy_, &inputs_slice_shape, &outputs_slice_shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer slice shape failed";
+    return FAILED;
+  }
+
+  if (inputs_slice_shape.empty() || outputs_slice_shape.empty()) {
+    MS_LOG(ERROR) << name_ << ": The slice shape of inputs or outputs is empty";
+    return FAILED;
+  }
+
+  Shape input_slice_shape = inputs_slice_shape[0];
+  Shape output_slice_shape = outputs_slice_shape[0];
+
+  TensorLayout input_tensor_layout, output_tensor_layout;
+  if (input_tensor_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], input_shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init tensor layout for input failed";
+    return FAILED;
+  }
+
+  if (output_tensor_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], output_shape) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Init tensor layout for output failed";
+    return FAILED;
+  }
+
+  TensorInfo input_tensor_info(input_tensor_layout, input_shape, input_slice_shape);
+  TensorInfo output_tensor_info(output_tensor_layout, output_shape, output_slice_shape);
+
+  inputs_tensor_info_.push_back(input_tensor_info);
+  outputs_tensor_info_.push_back(output_tensor_info);
+  return SUCCESS;
+}
+
+Status ExpandDimsInfo::InferMirrorOps() {
+  mirror_ops_.clear();
+
+  if (inputs_tensor_map_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The tensor map of inputs is empty";
+    return FAILED;
+  }
+
+  std::vector<Group> group;
+  if (CreateGroupByTensorMap(inputs_tensor_map_[0], &group) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Create group failed";
+    return FAILED;
+  }
+
+  if (group.empty()) {
+    MS_LOG(INFO) << name_ << ": No need to create mirror ops";
+    return SUCCESS;
+  }
+
+  OperatorVector mirror_op, placeholder_op;
+  mirror_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum());
+  mirror_ops_.push_back(mirror_op);
+  mirror_ops_.push_back(placeholder_op);
+  MS_LOG(INFO) << name_ << ": Create mirror ops success, the group name is " << group[0].name();
+  return SUCCESS;
+}
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/parallel/ops_info/activation_info.h

@@ -174,6 +174,26 @@ class NegInfo : public ActivationOther {
       : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
   ~NegInfo() override = default;
 };
+
+class ExpandDimsInfo : public ActivationOther {
+ public:
+  ExpandDimsInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+                 const PrimitiveAttrs& attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~ExpandDimsInfo() override = default;
+
+ protected:
+  Status GetAttrs() override;
+  Status InferTensorMap() override;
+  Status InferTensorInfo() override;
+  Status InferMirrorOps() override;
+  Status InferTensorStrategy();
+
+ private:
+  int32_t positive_axis_ = -1;
+  Strategys inputs_strategy_;
+  Strategys outputs_strategy_;
+};
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_OPTIMIZER_OPS_INFO_PARALLEL_ACTIVATION_INFO_H_

mindspore/ccsrc/parallel/ops_info/ops_utils.h

@@ -24,6 +24,8 @@ constexpr size_t PRELU_OUTPUTS_SIZE = 1;
 constexpr size_t PRELU_SECOND_INPUT_SIZE = 1;
 constexpr int32_t PRELU_CHANNEL_INDEX = 1;
 constexpr int32_t PRELU_CHANNEL_STRATEGY = 1;
+constexpr int32_t NO_SPLIT_MAP = -1;
+constexpr int32_t NO_SPLIT_STRATEGY = 1;
 constexpr size_t MATMUL_ATTRS_SIZE = 2;
 constexpr size_t MATMUL_INPUTS_SIZE = 2;
 constexpr size_t MATMUL_OUTPUTS_SIZE = 1;
@@ -31,6 +33,7 @@ constexpr size_t ACTIVATION_ATTR_SIZE = 1;
 constexpr size_t SOFTMAX_ATTR_SIZE = 1;
 constexpr size_t ACTIVATION_INPUTS_SIZE = 1;
 constexpr size_t ACTIVATION_OUTPUTS_SIZE = 1;
+constexpr size_t EXPANDDIMS_INPUT_SIZE = 2;
 constexpr size_t SoftmaxCrossEntropyWithLogitsAttrSize = 1;
 constexpr size_t SoftmaxCrossEntropyWithLogitsInputsSize = 2;
 constexpr size_t SoftmaxCrossEntropyWithLogitsOutputsSize = 2;
@@ -191,6 +194,7 @@ constexpr char GET_NEXT[] = "GetNext";
 constexpr char SQUEEZE[] = "Squeeze";
 constexpr char Neg[] = "Neg";
 constexpr char BATCH_MATMUL[] = "BatchMatMul";
+constexpr char EXPAND_DIMS[] = "ExpandDims";
 
 // Parallel don't care
 constexpr char TUPLE_GETITEM[] = "tuple_getitem";

mindspore/ccsrc/parallel/step_auto_parallel.cc

@@ -104,6 +104,7 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            CAST,
                                            Neg,
                                            BATCH_MATMUL,
+                                           EXPAND_DIMS,
                                            SQUEEZE};
 
 std::vector<std::string> elementwise_op_ = {ACTIVATION, GELU, TANH, SOFTMAX, LOG_SOFTMAX, RELU, SQRT,

tests/ut/python/parallel/test_expand_dims.py

@@ -0,0 +1,110 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+from mindspore.common.api import _executor
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None, strategy3=None):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.expand_dims = P.ExpandDims().set_strategy(strategy2)
+        self.mul2 = P.Mul().set_strategy(strategy3)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out = self.expand_dims(out, -1)
+        out = self.mul2(out, b)
+        return out
+
+
+class Net2(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super().__init__()
+        self.expand_dims = P.ExpandDims().set_strategy(strategy1)
+        self.mul = P.Mul().set_strategy(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.expand_dims(self.mul_weight, -1)
+        out = self.mul(out, b)
+        return out
+
+
+_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 32, 1]), dtype=ms.float32)
+
+
+def compile(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    _executor.compile(train_net, _x,  _b)
+    context.reset_auto_parallel_context()
+
+
+def test_expand_dims_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((16, 1, 1), )
+    strategy3 = ((16, 1, 1, 1), (16, 1, 1, 1))
+    net = Net(_w1, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_expand_dims_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 1, 16), (1, 1, 16))
+    strategy2 = ((1, 1, 16), )
+    strategy3 = ((1, 1, 16, 1), (1, 1, 16, 1))
+    net = Net(_w1, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_expand_dims_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((2, 2, 4), )
+    strategy3 = ((2, 2, 4, 1), (2, 2, 4, 1))
+    net = Net(_w1, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_expand_dims_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile(net)
+
+
+def test_expand_dims_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 4), (2, 2, 4))
+    strategy2 = ((1, 2, 2), )
+    strategy3 = ((2, 2, 4, 1), (2, 2, 4, 1))
+    net = Net(_w1, strategy1, strategy2, strategy3)
+    compile(net)
+
+
+def test_expand_dims_parameter():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 2, 2), )
+    strategy2 = ((2, 2, 4, 1), (2, 2, 4, 1))
+    net = Net2(_w1, strategy1, strategy2)
+    compile(net)